{"id":"W3154600543","doi":"10.24908/iqurcp.10293","title":"Chaotic Attractors in Tumour Growth","year":2018,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Attractor; Context (archaeology); Variety (cybernetics); Computer science; Set (abstract data type); Chaotic; Data science; Action (physics); Management science; Theoretical computer science; Cognitive science; Epistemology; Mathematics; Artificial intelligence; Biology; Psychology; Physics; Programming language; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004830417,0.0003581263,0.0003872636,0.0008708407,0.0004074098,0.001231018,0.0004430518,0.0007629915,0.001639915],"category_scores_gemma":[0.002987314,0.0001975362,0.0005123531,0.0004179203,0.001617512,0.001242167,0.001177961,0.0008767503,0.0001966126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064101,"about_ca_system_score_gemma":0.000426254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002000019,"about_ca_topic_score_gemma":0.0009067425,"domain_scores_codex":[0.9997925,0.00008474388,0.000009781211,0.00003127591,0.00005498551,0.00002672904],"domain_scores_gemma":[0.9991488,0.0004219929,0.0002345364,0.00005776786,0.0000738827,0.00006296301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002412467,0.00001120999,0.00142575,0.00008761371,0.00003222679,0.00019519,0.0003987735,0.2679197,0.002719658,0.7198068,0.001646499,0.005732427],"study_design_scores_gemma":[0.00001303965,0.00002643852,0.000988127,0.0000385667,0.00001109112,0.0001316183,0.0001469994,0.6545369,0.0005215549,0.3387511,0.004811057,0.00002355175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.457157,0.009659442,0.4280628,0.01286033,0.0004601729,0.00009726784,0.0003589277,0.000419427,0.09092456],"genre_scores_gemma":[0.980185,0.002365923,0.009778095,0.0001645595,0.0001231703,0.00005922672,0.00006762955,0.00003881809,0.007217659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002000019,"threshold_uncertainty_score":0.007720649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2003653631507326,"score_gpt":0.419906496770183,"score_spread":0.2195411336194504,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}